Machine Learning Methods & Theory

We actively develop new methodology and explore learning theory relevant to applications in medical imaging. Areas of research include meta-learning, multi-task & continual learning, causality, domain shift, geometric deep learning, semi-supervised and unsupervised learning, representation learning and Bayesian methods.

Representation Learning

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning (JMLR 2019)

Causality in Imaging

Causality matters in medical imaging (Nature Communications 2020)

Deep Structural Causal Models for Tractable Counterfactual Inference (NeurIPS 2020)

Machine Learning on Graphs

Overfitting & Class-Imbalance

Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation (MICCAI 2019)

Bayesian Deep Learning & Uncertainty Estimation

Implicit Weight Uncertainty in Neural Networks

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty (NeurIPS 2020)

Domain Shift

Domain Generalization via Model-Agnostic Learning of Semantic Features (NeurIPS 2019)

Unsupervised Domain Adaptation (IPMI 2017) and PnP-AdaNet (IEEE Access 2019)

Semi-Supervised Learning

Semi-supervised learning via compact latent space clustering (ICML 2018)

Multi-Task & Continual Learning

Towards continual learning in medical imaging (NeurIPS Workshop 2018).

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